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stringlengths 60
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def get_bin_indices ( self , values ) : return tuple ( [ self . get_axis_bin_index ( values [ ax_i ] , ax_i ) for ax_i in range ( self . dimensions ) ] )
| 1 |
python histogram get data with in bin
|
Returns index tuple in histogram of bin which contains value
|
cosqa-train-13200
|
def get_bin_indices(self, values):
"""Returns index tuple in histogram of bin which contains value"""
return tuple([self.get_axis_bin_index(values[ax_i], ax_i)
for ax_i in range(self.dimensions)])
|
def normalize_path ( path ) : return os . path . normcase ( os . path . realpath ( os . path . expanduser ( path ) ) )
| 1 |
file path username case sensitivity python
|
Convert a path to its canonical case - normalized absolute version .
|
cosqa-train-13201
|
def normalize_path(path):
"""
Convert a path to its canonical, case-normalized, absolute version.
"""
return os.path.normcase(os.path.realpath(os.path.expanduser(path)))
|
def getSize ( self ) : return self . widget . size [ 0 ] - self . border [ 0 ] * 2 , self . widget . size [ 1 ] - self . border [ 1 ] * 2
| 1 |
python how do i get a stacked widget height
|
Returns the size of the layer with the border size already subtracted .
|
cosqa-train-13202
|
def getSize(self):
"""
Returns the size of the layer, with the border size already subtracted.
"""
return self.widget.size[0]-self.border[0]*2,self.widget.size[1]-self.border[1]*2
|
def make_writeable ( filename ) : if not os . access ( filename , os . W_OK ) : st = os . stat ( filename ) new_permissions = stat . S_IMODE ( st . st_mode ) | stat . S_IWUSR os . chmod ( filename , new_permissions )
| 1 |
file permissions python write
|
Make sure that the file is writeable . Useful if our source is read - only .
|
cosqa-train-13203
|
def make_writeable(filename):
"""
Make sure that the file is writeable.
Useful if our source is read-only.
"""
if not os.access(filename, os.W_OK):
st = os.stat(filename)
new_permissions = stat.S_IMODE(st.st_mode) | stat.S_IWUSR
os.chmod(filename, new_permissions)
|
def get_image_dimension ( self , url ) : w_h = ( None , None ) try : if url . startswith ( '//' ) : url = 'http:' + url data = requests . get ( url ) . content im = Image . open ( BytesIO ( data ) ) w_h = im . size except Exception : logger . warning ( "Error getting image size {}" . format ( url ) , exc_info = True ) return w_h
| 1 |
python how do i get the image size
|
Return a tuple that contains ( width height ) Pass in a url to an image and find out its size without loading the whole file If the image wxh could not be found the tuple will contain None values
|
cosqa-train-13204
|
def get_image_dimension(self, url):
"""
Return a tuple that contains (width, height)
Pass in a url to an image and find out its size without loading the whole file
If the image wxh could not be found, the tuple will contain `None` values
"""
w_h = (None, None)
try:
if url.startswith('//'):
url = 'http:' + url
data = requests.get(url).content
im = Image.open(BytesIO(data))
w_h = im.size
except Exception:
logger.warning("Error getting image size {}".format(url), exc_info=True)
return w_h
|
def filter_bolts ( table , header ) : bolts_info = [ ] for row in table : if row [ 0 ] == 'bolt' : bolts_info . append ( row ) return bolts_info , header
| 1 |
filer the values of a table in python based upon variable
|
filter to keep bolts
|
cosqa-train-13205
|
def filter_bolts(table, header):
""" filter to keep bolts """
bolts_info = []
for row in table:
if row[0] == 'bolt':
bolts_info.append(row)
return bolts_info, header
|
def is_array ( self , key ) : data = self . model . get_data ( ) return isinstance ( data [ key ] , ( ndarray , MaskedArray ) )
| 1 |
python how do you check the array attribute
|
Return True if variable is a numpy array
|
cosqa-train-13206
|
def is_array(self, key):
"""Return True if variable is a numpy array"""
data = self.model.get_data()
return isinstance(data[key], (ndarray, MaskedArray))
|
def clean_dataframe ( df ) : df = df . fillna ( method = 'ffill' ) df = df . fillna ( 0.0 ) return df
| 1 |
fill is null with other columns python
|
Fill NaNs with the previous value the next value or if all are NaN then 1 . 0
|
cosqa-train-13207
|
def clean_dataframe(df):
"""Fill NaNs with the previous value, the next value or if all are NaN then 1.0"""
df = df.fillna(method='ffill')
df = df.fillna(0.0)
return df
|
def chunks ( dictionary , chunk_size ) : iterable = iter ( dictionary ) for __ in range ( 0 , len ( dictionary ) , chunk_size ) : yield { key : dictionary [ key ] for key in islice ( iterable , chunk_size ) }
| 1 |
python how do you split a dictionary into evenly sized chunks
|
Yield successive n - sized chunks from dictionary .
|
cosqa-train-13208
|
def chunks(dictionary, chunk_size):
"""
Yield successive n-sized chunks from dictionary.
"""
iterable = iter(dictionary)
for __ in range(0, len(dictionary), chunk_size):
yield {key: dictionary[key] for key in islice(iterable, chunk_size)}
|
def inpaint ( self ) : import inpaint filled = inpaint . replace_nans ( np . ma . filled ( self . raster_data , np . NAN ) . astype ( np . float32 ) , 3 , 0.01 , 2 ) self . raster_data = np . ma . masked_invalid ( filled )
| 1 |
filling around an image with white python
|
Replace masked - out elements in an array using an iterative image inpainting algorithm .
|
cosqa-train-13209
|
def inpaint(self):
""" Replace masked-out elements in an array using an iterative image inpainting algorithm. """
import inpaint
filled = inpaint.replace_nans(np.ma.filled(self.raster_data, np.NAN).astype(np.float32), 3, 0.01, 2)
self.raster_data = np.ma.masked_invalid(filled)
|
def quit ( self ) : logger . debug ( "ArgosApplication.quit called" ) assert len ( self . mainWindows ) == 0 , "Bug: still {} windows present at application quit!" . format ( len ( self . mainWindows ) ) self . qApplication . quit ( )
| 1 |
python how not to close the window after running
|
Quits the application ( called when the last window is closed )
|
cosqa-train-13210
|
def quit(self):
""" Quits the application (called when the last window is closed)
"""
logger.debug("ArgosApplication.quit called")
assert len(self.mainWindows) == 0, \
"Bug: still {} windows present at application quit!".format(len(self.mainWindows))
self.qApplication.quit()
|
def stringify_col ( df , col_name ) : df = df . copy ( ) df [ col_name ] = df [ col_name ] . fillna ( "" ) df [ col_name ] = df [ col_name ] . astype ( str ) return df
| 1 |
fillna with string for specific columnin python
|
Take a dataframe and string - i - fy a column of values . Turn nan / None into and all other values into strings .
|
cosqa-train-13211
|
def stringify_col(df, col_name):
"""
Take a dataframe and string-i-fy a column of values.
Turn nan/None into "" and all other values into strings.
Parameters
----------
df : dataframe
col_name : string
"""
df = df.copy()
df[col_name] = df[col_name].fillna("")
df[col_name] = df[col_name].astype(str)
return df
|
def extend_with ( func ) : if not func . __name__ in ArgParseInator . _plugins : ArgParseInator . _plugins [ func . __name__ ] = func
| 0 |
python how to add builtin
|
Extends with class or function
|
cosqa-train-13212
|
def extend_with(func):
"""Extends with class or function"""
if not func.__name__ in ArgParseInator._plugins:
ArgParseInator._plugins[func.__name__] = func
|
def filter_dict ( d , keys ) : return { k : v for k , v in d . items ( ) if k in keys }
| 1 |
filter dictionary python with certain keys
|
Creates a new dict from an existing dict that only has the given keys
|
cosqa-train-13213
|
def filter_dict(d, keys):
"""
Creates a new dict from an existing dict that only has the given keys
"""
return {k: v for k, v in d.items() if k in keys}
|
def _transform_col ( self , x , i ) : return x . fillna ( NAN_INT ) . map ( self . label_encoders [ i ] ) . fillna ( 0 )
| 1 |
python how to apply label encoder all all columns
|
Encode one categorical column into labels .
|
cosqa-train-13214
|
def _transform_col(self, x, i):
"""Encode one categorical column into labels.
Args:
x (pandas.Series): a categorical column to encode
i (int): column index
Returns:
x (pandas.Series): a column with labels.
"""
return x.fillna(NAN_INT).map(self.label_encoders[i]).fillna(0)
|
def twitter_timeline ( screen_name , since_id = None ) : consumer_key = twitter_credential ( 'consumer_key' ) consumer_secret = twitter_credential ( 'consumer_secret' ) access_token = twitter_credential ( 'access_token' ) access_token_secret = twitter_credential ( 'access_secret' ) auth = tweepy . OAuthHandler ( consumer_key , consumer_secret ) auth . set_access_token ( access_token , access_token_secret ) api = tweepy . API ( auth ) return get_all_tweets ( screen_name , api , since_id )
| 1 |
filter twitter user tweepy python
|
Return relevant twitter timeline
|
cosqa-train-13215
|
def twitter_timeline(screen_name, since_id=None):
""" Return relevant twitter timeline """
consumer_key = twitter_credential('consumer_key')
consumer_secret = twitter_credential('consumer_secret')
access_token = twitter_credential('access_token')
access_token_secret = twitter_credential('access_secret')
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
return get_all_tweets(screen_name, api, since_id)
|
def round_data ( filter_data ) : for index , _ in enumerate ( filter_data ) : filter_data [ index ] [ 0 ] = round ( filter_data [ index ] [ 0 ] / 100.0 ) * 100.0 return filter_data
| 0 |
python how to apply round in list value
|
round the data
|
cosqa-train-13216
|
def round_data(filter_data):
""" round the data"""
for index, _ in enumerate(filter_data):
filter_data[index][0] = round(filter_data[index][0] / 100.0) * 100.0
return filter_data
|
def binSearch ( arr , val ) : i = bisect_left ( arr , val ) if i != len ( arr ) and arr [ i ] == val : return i return - 1
| 1 |
finding index of a specific element in a list python
|
Function for running binary search on a sorted list .
|
cosqa-train-13217
|
def binSearch(arr, val):
"""
Function for running binary search on a sorted list.
:param arr: (list) a sorted list of integers to search
:param val: (int) a integer to search for in the sorted array
:returns: (int) the index of the element if it is found and -1 otherwise.
"""
i = bisect_left(arr, val)
if i != len(arr) and arr[i] == val:
return i
return -1
|
def _bind_parameter ( self , parameter , value ) : for ( instr , param_index ) in self . _parameter_table [ parameter ] : instr . params [ param_index ] = value
| 1 |
python how to bind paramters to function
|
Assigns a parameter value to matching instructions in - place .
|
cosqa-train-13218
|
def _bind_parameter(self, parameter, value):
"""Assigns a parameter value to matching instructions in-place."""
for (instr, param_index) in self._parameter_table[parameter]:
instr.params[param_index] = value
|
def index_nearest ( value , array ) : a = ( array - value ) ** 2 return index ( a . min ( ) , a )
| 1 |
finding nearest numbers in python
|
expects a _n . array returns the global minimum of ( value - array ) ^2
|
cosqa-train-13219
|
def index_nearest(value, array):
"""
expects a _n.array
returns the global minimum of (value-array)^2
"""
a = (array-value)**2
return index(a.min(), a)
|
def lower_ext ( abspath ) : fname , ext = os . path . splitext ( abspath ) return fname + ext . lower ( )
| 1 |
python how to change file extension
|
Convert file extension to lowercase .
|
cosqa-train-13220
|
def lower_ext(abspath):
"""Convert file extension to lowercase.
"""
fname, ext = os.path.splitext(abspath)
return fname + ext.lower()
|
def iter_finds ( regex_obj , s ) : if isinstance ( regex_obj , str ) : for m in re . finditer ( regex_obj , s ) : yield m . group ( ) else : for m in regex_obj . finditer ( s ) : yield m . group ( )
| 1 |
finding patterns in python string
|
Generate all matches found within a string for a regex and yield each match as a string
|
cosqa-train-13221
|
def iter_finds(regex_obj, s):
"""Generate all matches found within a string for a regex and yield each match as a string"""
if isinstance(regex_obj, str):
for m in re.finditer(regex_obj, s):
yield m.group()
else:
for m in regex_obj.finditer(s):
yield m.group()
|
def _pip_exists ( self ) : return os . path . isfile ( os . path . join ( self . path , 'bin' , 'pip' ) )
| 1 |
python how to check if environment defined
|
Returns True if pip exists inside the virtual environment . Can be used as a naive way to verify that the environment is installed .
|
cosqa-train-13222
|
def _pip_exists(self):
"""Returns True if pip exists inside the virtual environment. Can be
used as a naive way to verify that the environment is installed."""
return os.path.isfile(os.path.join(self.path, 'bin', 'pip'))
|
def check_if_numbers_are_consecutive ( list_ ) : return all ( ( True if second - first == 1 else False for first , second in zip ( list_ [ : - 1 ] , list_ [ 1 : ] ) ) )
| 1 |
finding sets of consecutive numbers in a list python
|
Returns True if numbers in the list are consecutive
|
cosqa-train-13223
|
def check_if_numbers_are_consecutive(list_):
"""
Returns True if numbers in the list are consecutive
:param list_: list of integers
:return: Boolean
"""
return all((True if second - first == 1 else False
for first, second in zip(list_[:-1], list_[1:])))
|
def class_check ( vector ) : for i in vector : if not isinstance ( i , type ( vector [ 0 ] ) ) : return False return True
| 1 |
python how to check list or array
|
Check different items in matrix classes .
|
cosqa-train-13224
|
def class_check(vector):
"""
Check different items in matrix classes.
:param vector: input vector
:type vector : list
:return: bool
"""
for i in vector:
if not isinstance(i, type(vector[0])):
return False
return True
|
def get_previous ( self ) : return BillingCycle . objects . filter ( date_range__lt = self . date_range ) . order_by ( 'date_range' ) . last ( )
| 0 |
finding the most recent date before a given date python
|
Get the billing cycle prior to this one . May return None
|
cosqa-train-13225
|
def get_previous(self):
"""Get the billing cycle prior to this one. May return None"""
return BillingCycle.objects.filter(date_range__lt=self.date_range).order_by('date_range').last()
|
def pid_exists ( pid ) : try : os . kill ( pid , 0 ) except OSError as exc : return exc . errno == errno . EPERM else : return True
| 1 |
python how to check whether the process with pid exist
|
Determines if a system process identifer exists in process table .
|
cosqa-train-13226
|
def pid_exists(pid):
""" Determines if a system process identifer exists in process table.
"""
try:
os.kill(pid, 0)
except OSError as exc:
return exc.errno == errno.EPERM
else:
return True
|
def closest ( xarr , val ) : idx_closest = np . argmin ( np . abs ( np . array ( xarr ) - val ) ) return idx_closest
| 0 |
finding the two closest values in an array python
|
Return the index of the closest in xarr to value val
|
cosqa-train-13227
|
def closest(xarr, val):
""" Return the index of the closest in xarr to value val """
idx_closest = np.argmin(np.abs(np.array(xarr) - val))
return idx_closest
|
def Flush ( self ) : while self . _age : node = self . _age . PopLeft ( ) self . KillObject ( node . data ) self . _hash = dict ( )
| 1 |
python how to clear memory
|
Flush all items from cache .
|
cosqa-train-13228
|
def Flush(self):
"""Flush all items from cache."""
while self._age:
node = self._age.PopLeft()
self.KillObject(node.data)
self._hash = dict()
|
def findMax ( arr ) : out = np . zeros ( shape = arr . shape , dtype = bool ) _calcMax ( arr , out ) return out
| 1 |
fins max in array python
|
in comparison to argrelmax () more simple and reliable peak finder
|
cosqa-train-13229
|
def findMax(arr):
"""
in comparison to argrelmax() more simple and reliable peak finder
"""
out = np.zeros(shape=arr.shape, dtype=bool)
_calcMax(arr, out)
return out
|
def is_equal_strings_ignore_case ( first , second ) : if first and second : return first . upper ( ) == second . upper ( ) else : return not ( first or second )
| 0 |
python how to compare strings without case
|
The function compares strings ignoring case
|
cosqa-train-13230
|
def is_equal_strings_ignore_case(first, second):
"""The function compares strings ignoring case"""
if first and second:
return first.upper() == second.upper()
else:
return not (first or second)
|
def fit_gaussian ( x , y , yerr , p0 ) : try : popt , pcov = curve_fit ( gaussian , x , y , sigma = yerr , p0 = p0 , absolute_sigma = True ) except RuntimeError : return [ 0 ] , [ 0 ] return popt , pcov
| 1 |
fitting a gaussian in python direct method
|
Fit a Gaussian to the data
|
cosqa-train-13231
|
def fit_gaussian(x, y, yerr, p0):
""" Fit a Gaussian to the data """
try:
popt, pcov = curve_fit(gaussian, x, y, sigma=yerr, p0=p0, absolute_sigma=True)
except RuntimeError:
return [0],[0]
return popt, pcov
|
def chi_square_calc ( classes , table , TOP , P , POP ) : try : result = 0 for i in classes : for index , j in enumerate ( classes ) : expected = ( TOP [ j ] * P [ i ] ) / ( POP [ i ] ) result += ( ( table [ i ] [ j ] - expected ) ** 2 ) / expected return result except Exception : return "None"
| 1 |
python how to compute chi square
|
Calculate chi - squared .
|
cosqa-train-13232
|
def chi_square_calc(classes, table, TOP, P, POP):
"""
Calculate chi-squared.
:param classes: confusion matrix classes
:type classes : list
:param table: confusion matrix table
:type table : dict
:param TOP: test outcome positive
:type TOP : dict
:param P: condition positive
:type P : dict
:param POP: population
:type POP : dict
:return: chi-squared as float
"""
try:
result = 0
for i in classes:
for index, j in enumerate(classes):
expected = (TOP[j] * P[i]) / (POP[i])
result += ((table[i][j] - expected)**2) / expected
return result
except Exception:
return "None"
|
def sbatch_template ( self ) : template = self . sbatch_template_str if template . startswith ( '#!' ) : # script is embedded in YAML return jinja_environment . from_string ( template ) return jinja_environment . get_template ( template )
| 1 |
flask jinja if get python
|
: return Jinja sbatch template for the current tag
|
cosqa-train-13233
|
def sbatch_template(self):
""":return Jinja sbatch template for the current tag"""
template = self.sbatch_template_str
if template.startswith('#!'):
# script is embedded in YAML
return jinja_environment.from_string(template)
return jinja_environment.get_template(template)
|
def be_array_from_bytes ( fmt , data ) : arr = array . array ( str ( fmt ) , data ) return fix_byteorder ( arr )
| 1 |
python how to covert binary to byte arry
|
Reads an array from bytestring with big - endian data .
|
cosqa-train-13234
|
def be_array_from_bytes(fmt, data):
"""
Reads an array from bytestring with big-endian data.
"""
arr = array.array(str(fmt), data)
return fix_byteorder(arr)
|
def _change_height ( self , ax , new_value ) : for patch in ax . patches : current_height = patch . get_height ( ) diff = current_height - new_value # we change the bar height patch . set_height ( new_value ) # we recenter the bar patch . set_y ( patch . get_y ( ) + diff * .5 )
| 1 |
flexibility for barwidth in python matplotlib barplot
|
Make bars in horizontal bar chart thinner
|
cosqa-train-13235
|
def _change_height(self, ax, new_value):
"""Make bars in horizontal bar chart thinner"""
for patch in ax.patches:
current_height = patch.get_height()
diff = current_height - new_value
# we change the bar height
patch.set_height(new_value)
# we recenter the bar
patch.set_y(patch.get_y() + diff * .5)
|
def auto_update ( cls , function ) : def wrapper ( self , * args , * * kwargs ) : f = function ( self , * args , * * kwargs ) self . update ( ) return f return wrapper
| 1 |
python how to decorate for both instance methods
|
This class method could be used as decorator on subclasses it ensures update method is called after function execution .
|
cosqa-train-13236
|
def auto_update(cls, function):
"""
This class method could be used as decorator on subclasses, it ensures
update method is called after function execution.
"""
def wrapper(self, *args, **kwargs):
f = function(self, *args, **kwargs)
self.update()
return f
return wrapper
|
def hflip ( img ) : if not _is_pil_image ( img ) : raise TypeError ( 'img should be PIL Image. Got {}' . format ( type ( img ) ) ) return img . transpose ( Image . FLIP_LEFT_RIGHT )
| 1 |
flip image using python
|
Horizontally flip the given PIL Image .
|
cosqa-train-13237
|
def hflip(img):
"""Horizontally flip the given PIL Image.
Args:
img (PIL Image): Image to be flipped.
Returns:
PIL Image: Horizontall flipped image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
return img.transpose(Image.FLIP_LEFT_RIGHT)
|
def _delete_local ( self , filename ) : if os . path . exists ( filename ) : os . remove ( filename )
| 1 |
python how to delete files on local disk
|
Deletes the specified file from the local filesystem .
|
cosqa-train-13238
|
def _delete_local(self, filename):
"""Deletes the specified file from the local filesystem."""
if os.path.exists(filename):
os.remove(filename)
|
def safe_exit ( output ) : try : sys . stdout . write ( output ) sys . stdout . flush ( ) except IOError : pass
| 1 |
flush standard output python
|
exit without breaking pipes .
|
cosqa-train-13239
|
def safe_exit(output):
"""exit without breaking pipes."""
try:
sys.stdout.write(output)
sys.stdout.flush()
except IOError:
pass
|
def _platform_is_windows ( platform = sys . platform ) : matched = platform in ( 'cygwin' , 'win32' , 'win64' ) if matched : error_msg = "Windows isn't supported yet" raise OSError ( error_msg ) return matched
| 0 |
python how to determine if linux or windows
|
Is the current OS a Windows?
|
cosqa-train-13240
|
def _platform_is_windows(platform=sys.platform):
"""Is the current OS a Windows?"""
matched = platform in ('cygwin', 'win32', 'win64')
if matched:
error_msg = "Windows isn't supported yet"
raise OSError(error_msg)
return matched
|
def get_page_and_url ( session , url ) : reply = get_reply ( session , url ) return reply . text , reply . url
| 1 |
follow redirection to get actual link in python
|
Download an HTML page using the requests session and return the final URL after following redirects .
|
cosqa-train-13241
|
def get_page_and_url(session, url):
"""
Download an HTML page using the requests session and return
the final URL after following redirects.
"""
reply = get_reply(session, url)
return reply.text, reply.url
|
def compute_boxplot ( self , series ) : from matplotlib . cbook import boxplot_stats series = series [ series . notnull ( ) ] if len ( series . values ) == 0 : return { } elif not is_numeric_dtype ( series ) : return self . non_numeric_stats ( series ) stats = boxplot_stats ( list ( series . values ) ) [ 0 ] stats [ 'count' ] = len ( series . values ) stats [ 'fliers' ] = "|" . join ( map ( str , stats [ 'fliers' ] ) ) return stats
| 1 |
python how to do a boxplot
|
Compute boxplot for given pandas Series .
|
cosqa-train-13242
|
def compute_boxplot(self, series):
"""
Compute boxplot for given pandas Series.
"""
from matplotlib.cbook import boxplot_stats
series = series[series.notnull()]
if len(series.values) == 0:
return {}
elif not is_numeric_dtype(series):
return self.non_numeric_stats(series)
stats = boxplot_stats(list(series.values))[0]
stats['count'] = len(series.values)
stats['fliers'] = "|".join(map(str, stats['fliers']))
return stats
|
def serialize ( self , value , * * kwargs ) : return [ self . item_type . serialize ( val , * * kwargs ) for val in value ]
| 1 |
for in python unhashable type list
|
Serialize every item of the list .
|
cosqa-train-13243
|
def serialize(self, value, **kwargs):
"""Serialize every item of the list."""
return [self.item_type.serialize(val, **kwargs) for val in value]
|
def unit_tangent ( self , t ) : dseg = self . derivative ( t ) return dseg / abs ( dseg )
| 0 |
python how to do tangent
|
returns the unit tangent vector of the segment at t ( centered at the origin and expressed as a complex number ) .
|
cosqa-train-13244
|
def unit_tangent(self, t):
"""returns the unit tangent vector of the segment at t (centered at
the origin and expressed as a complex number)."""
dseg = self.derivative(t)
return dseg/abs(dseg)
|
def find_frequencies ( data , freq = 44100 , bits = 16 ) : # Fast fourier transform n = len ( data ) p = _fft ( data ) uniquePts = numpy . ceil ( ( n + 1 ) / 2.0 ) # Scale by the length (n) and square the value to get the amplitude p = [ ( abs ( x ) / float ( n ) ) ** 2 * 2 for x in p [ 0 : uniquePts ] ] p [ 0 ] = p [ 0 ] / 2 if n % 2 == 0 : p [ - 1 ] = p [ - 1 ] / 2 # Generate the frequencies and zip with the amplitudes s = freq / float ( n ) freqArray = numpy . arange ( 0 , uniquePts * s , s ) return zip ( freqArray , p )
| 1 |
fourier transform audio file python
|
Convert audio data into a frequency - amplitude table using fast fourier transformation .
|
cosqa-train-13245
|
def find_frequencies(data, freq=44100, bits=16):
"""Convert audio data into a frequency-amplitude table using fast fourier
transformation.
Return a list of tuples (frequency, amplitude).
Data should only contain one channel of audio.
"""
# Fast fourier transform
n = len(data)
p = _fft(data)
uniquePts = numpy.ceil((n + 1) / 2.0)
# Scale by the length (n) and square the value to get the amplitude
p = [(abs(x) / float(n)) ** 2 * 2 for x in p[0:uniquePts]]
p[0] = p[0] / 2
if n % 2 == 0:
p[-1] = p[-1] / 2
# Generate the frequencies and zip with the amplitudes
s = freq / float(n)
freqArray = numpy.arange(0, uniquePts * s, s)
return zip(freqArray, p)
|
def autoscan ( ) : for port in serial . tools . list_ports . comports ( ) : if is_micropython_usb_device ( port ) : connect_serial ( port [ 0 ] )
| 1 |
python how to enumerate serial ports in linux usb hub
|
autoscan will check all of the serial ports to see if they have a matching VID : PID for a MicroPython board .
|
cosqa-train-13246
|
def autoscan():
"""autoscan will check all of the serial ports to see if they have
a matching VID:PID for a MicroPython board.
"""
for port in serial.tools.list_ports.comports():
if is_micropython_usb_device(port):
connect_serial(port[0])
|
def parse_float ( float_str ) : factor = __get_factor ( float_str ) if factor != 1 : float_str = float_str [ : - 1 ] try : return float ( float_str . replace ( ',' , '' ) ) * factor except ValueError : return None
| 1 |
fraction and whole number string to float python
|
Parse a string of the form 305 . 48b into a Python float . The terminal letter if present indicates e . g . billions .
|
cosqa-train-13247
|
def parse_float(float_str):
"""Parse a string of the form 305.48b into a Python float.
The terminal letter, if present, indicates e.g. billions."""
factor = __get_factor(float_str)
if factor != 1:
float_str = float_str[:-1]
try:
return float(float_str.replace(',', '')) * factor
except ValueError:
return None
|
def cli ( ctx , project_dir ) : exit_code = SCons ( project_dir ) . clean ( ) ctx . exit ( exit_code )
| 1 |
python how to exit a project
|
Clean the previous generated files .
|
cosqa-train-13248
|
def cli(ctx, project_dir):
"""Clean the previous generated files."""
exit_code = SCons(project_dir).clean()
ctx.exit(exit_code)
|
def connect ( ) : ftp_class = ftplib . FTP if not SSL else ftplib . FTP_TLS ftp = ftp_class ( timeout = TIMEOUT ) ftp . connect ( HOST , PORT ) ftp . login ( USER , PASSWORD ) if SSL : ftp . prot_p ( ) # secure data connection return ftp
| 1 |
free servers for testing ftp in python
|
Connect to FTP server login and return an ftplib . FTP instance .
|
cosqa-train-13249
|
def connect():
"""Connect to FTP server, login and return an ftplib.FTP instance."""
ftp_class = ftplib.FTP if not SSL else ftplib.FTP_TLS
ftp = ftp_class(timeout=TIMEOUT)
ftp.connect(HOST, PORT)
ftp.login(USER, PASSWORD)
if SSL:
ftp.prot_p() # secure data connection
return ftp
|
def fmt_duration ( secs ) : return ' ' . join ( fmt . human_duration ( secs , 0 , precision = 2 , short = True ) . strip ( ) . split ( ) )
| 1 |
python how to format a duration in seconds
|
Format a duration in seconds .
|
cosqa-train-13250
|
def fmt_duration(secs):
"""Format a duration in seconds."""
return ' '.join(fmt.human_duration(secs, 0, precision=2, short=True).strip().split())
|
def get_lons_from_cartesian ( x__ , y__ ) : return rad2deg ( arccos ( x__ / sqrt ( x__ ** 2 + y__ ** 2 ) ) ) * sign ( y__ )
| 0 |
from polar to lat lon python
|
Get longitudes from cartesian coordinates .
|
cosqa-train-13251
|
def get_lons_from_cartesian(x__, y__):
"""Get longitudes from cartesian coordinates.
"""
return rad2deg(arccos(x__ / sqrt(x__ ** 2 + y__ ** 2))) * sign(y__)
|
def _digits ( minval , maxval ) : if minval == maxval : return 3 else : return min ( 10 , max ( 2 , int ( 1 + abs ( np . log10 ( maxval - minval ) ) ) ) )
| 1 |
python how to get a decimal range
|
Digits needed to comforatbly display values in [ minval maxval ]
|
cosqa-train-13252
|
def _digits(minval, maxval):
"""Digits needed to comforatbly display values in [minval, maxval]"""
if minval == maxval:
return 3
else:
return min(10, max(2, int(1 + abs(np.log10(maxval - minval)))))
|
def _bytes_to_json ( value ) : if isinstance ( value , bytes ) : value = base64 . standard_b64encode ( value ) . decode ( "ascii" ) return value
| 1 |
from python json to bytes
|
Coerce value to an JSON - compatible representation .
|
cosqa-train-13253
|
def _bytes_to_json(value):
"""Coerce 'value' to an JSON-compatible representation."""
if isinstance(value, bytes):
value = base64.standard_b64encode(value).decode("ascii")
return value
|
def _num_cpus_darwin ( ) : p = subprocess . Popen ( [ 'sysctl' , '-n' , 'hw.ncpu' ] , stdout = subprocess . PIPE ) return p . stdout . read ( )
| 1 |
python how to get number of cores on computer
|
Return the number of active CPUs on a Darwin system .
|
cosqa-train-13254
|
def _num_cpus_darwin():
"""Return the number of active CPUs on a Darwin system."""
p = subprocess.Popen(['sysctl','-n','hw.ncpu'],stdout=subprocess.PIPE)
return p.stdout.read()
|
def get_table_width ( table ) : columns = transpose_table ( prepare_rows ( table ) ) widths = [ max ( len ( cell ) for cell in column ) for column in columns ] return len ( '+' + '|' . join ( '-' * ( w + 2 ) for w in widths ) + '+' )
| 1 |
function for width of table in python
|
Gets the width of the table that would be printed . : rtype : int
|
cosqa-train-13255
|
def get_table_width(table):
"""
Gets the width of the table that would be printed.
:rtype: ``int``
"""
columns = transpose_table(prepare_rows(table))
widths = [max(len(cell) for cell in column) for column in columns]
return len('+' + '|'.join('-' * (w + 2) for w in widths) + '+')
|
def find_root ( self ) : cmd = self while cmd . parent : cmd = cmd . parent return cmd
| 1 |
python how to get parent of self
|
Traverse parent refs to top .
|
cosqa-train-13256
|
def find_root(self):
""" Traverse parent refs to top. """
cmd = self
while cmd.parent:
cmd = cmd.parent
return cmd
|
def _fullname ( o ) : return o . __module__ + "." + o . __name__ if o . __module__ else o . __name__
| 1 |
function name to string python
|
Return the fully - qualified name of a function .
|
cosqa-train-13257
|
def _fullname(o):
"""Return the fully-qualified name of a function."""
return o.__module__ + "." + o.__name__ if o.__module__ else o.__name__
|
def get_obj_cols ( df ) : obj_cols = [ ] for idx , dt in enumerate ( df . dtypes ) : if dt == 'object' or is_category ( dt ) : obj_cols . append ( df . columns . values [ idx ] ) return obj_cols
| 1 |
python how to get the names of columns in data frame
|
Returns names of object columns in the DataFrame .
|
cosqa-train-13258
|
def get_obj_cols(df):
"""
Returns names of 'object' columns in the DataFrame.
"""
obj_cols = []
for idx, dt in enumerate(df.dtypes):
if dt == 'object' or is_category(dt):
obj_cols.append(df.columns.values[idx])
return obj_cols
|
def flatten_list ( l ) : return list ( chain . from_iterable ( repeat ( x , 1 ) if isinstance ( x , str ) else x for x in l ) )
| 1 |
function python several lists to one list
|
Nested lists to single - level list does not split strings
|
cosqa-train-13259
|
def flatten_list(l):
""" Nested lists to single-level list, does not split strings"""
return list(chain.from_iterable(repeat(x,1) if isinstance(x,str) else x for x in l))
|
def count_ ( self ) : try : num = len ( self . df . index ) except Exception as e : self . err ( e , "Can not count data" ) return return num
| 1 |
python how to get the number of rows of a data frame
|
Returns the number of rows of the main dataframe
|
cosqa-train-13260
|
def count_(self):
"""
Returns the number of rows of the main dataframe
"""
try:
num = len(self.df.index)
except Exception as e:
self.err(e, "Can not count data")
return
return num
|
def apply ( self , func , args = ( ) , kwds = dict ( ) ) : return self . apply_async ( func , args , kwds ) . get ( )
| 1 |
function return apply async python
|
Equivalent of the apply () builtin function . It blocks till the result is ready .
|
cosqa-train-13261
|
def apply(self, func, args=(), kwds=dict()):
"""Equivalent of the apply() builtin function. It blocks till
the result is ready."""
return self.apply_async(func, args, kwds).get()
|
def _check_task_id ( self , context ) : ti = context [ 'ti' ] celery_result = ti . xcom_pull ( task_ids = self . target_task_id ) return celery_result . ready ( )
| 1 |
python how to get the progress of celery task
|
Gets the returned Celery result from the Airflow task ID provided to the sensor and returns True if the celery result has been finished execution .
|
cosqa-train-13262
|
def _check_task_id(self, context):
"""
Gets the returned Celery result from the Airflow task
ID provided to the sensor, and returns True if the
celery result has been finished execution.
:param context: Airflow's execution context
:type context: dict
:return: True if task has been executed, otherwise False
:rtype: bool
"""
ti = context['ti']
celery_result = ti.xcom_pull(task_ids=self.target_task_id)
return celery_result.ready()
|
def _file_exists ( path , filename ) : return os . path . isfile ( os . path . join ( path , filename ) )
| 1 |
function to check file existence in python
|
Checks if the filename exists under the path .
|
cosqa-train-13263
|
def _file_exists(path, filename):
"""Checks if the filename exists under the path."""
return os.path.isfile(os.path.join(path, filename))
|
def to_tree ( self ) : tree = TreeLibTree ( ) for node in self : tree . create_node ( node , node . node_id , parent = node . parent ) return tree
| 1 |
python how to get tree object
|
returns a TreeLib tree
|
cosqa-train-13264
|
def to_tree(self):
""" returns a TreeLib tree """
tree = TreeLibTree()
for node in self:
tree.create_node(node, node.node_id, parent=node.parent)
return tree
|
def test3 ( ) : import time p = MVisionProcess ( ) p . start ( ) time . sleep ( 5 ) p . stop ( )
| 1 |
function to repeat process in python 3
|
Test the multiprocess
|
cosqa-train-13265
|
def test3():
"""Test the multiprocess
"""
import time
p = MVisionProcess()
p.start()
time.sleep(5)
p.stop()
|
def hash_iterable ( it ) : hash_value = hash ( type ( it ) ) for value in it : hash_value = hash ( ( hash_value , value ) ) return hash_value
| 0 |
python how to hash tuple
|
Perform a O ( 1 ) memory hash of an iterable of arbitrary length .
|
cosqa-train-13266
|
def hash_iterable(it):
"""Perform a O(1) memory hash of an iterable of arbitrary length.
hash(tuple(it)) creates a temporary tuple containing all values from it
which could be a problem if it is large.
See discussion at:
https://groups.google.com/forum/#!msg/python-ideas/XcuC01a8SYs/e-doB9TbDwAJ
"""
hash_value = hash(type(it))
for value in it:
hash_value = hash((hash_value, value))
return hash_value
|
def gauss_pdf ( x , mu , sigma ) : return 1 / np . sqrt ( 2 * np . pi ) / sigma * np . exp ( - ( x - mu ) ** 2 / 2. / sigma ** 2 )
| 1 |
gaussian distribution code in python
|
Normalized Gaussian
|
cosqa-train-13267
|
def gauss_pdf(x, mu, sigma):
"""Normalized Gaussian"""
return 1 / np.sqrt(2 * np.pi) / sigma * np.exp(-(x - mu) ** 2 / 2. / sigma ** 2)
|
def handle_m2m ( self , sender , instance , * * kwargs ) : self . handle_save ( instance . __class__ , instance )
| 0 |
python how to implement one to many association
|
Handle many to many relationships
|
cosqa-train-13268
|
def handle_m2m(self, sender, instance, **kwargs):
""" Handle many to many relationships """
self.handle_save(instance.__class__, instance)
|
def gaussian_kernel ( sigma , truncate = 4.0 ) : sigma = float ( sigma ) radius = int ( truncate * sigma + 0.5 ) x , y = np . mgrid [ - radius : radius + 1 , - radius : radius + 1 ] sigma = sigma ** 2 k = 2 * np . exp ( - 0.5 * ( x ** 2 + y ** 2 ) / sigma ) k = k / np . sum ( k ) return k
| 1 |
gaussian kernel python with sigma and width
|
Return Gaussian that truncates at the given number of std deviations .
|
cosqa-train-13269
|
def gaussian_kernel(sigma, truncate=4.0):
"""Return Gaussian that truncates at the given number of std deviations.
Adapted from https://github.com/nicjhan/gaussian-filter
"""
sigma = float(sigma)
radius = int(truncate * sigma + 0.5)
x, y = np.mgrid[-radius:radius + 1, -radius:radius + 1]
sigma = sigma ** 2
k = 2 * np.exp(-0.5 * (x ** 2 + y ** 2) / sigma)
k = k / np.sum(k)
return k
|
def do_next ( self , args ) : self . _do_print_from_last_cmd = True self . _interp . step_over ( ) return True
| 1 |
python how to jump to next loop
|
Step over the next statement
|
cosqa-train-13270
|
def do_next(self, args):
"""Step over the next statement
"""
self._do_print_from_last_cmd = True
self._interp.step_over()
return True
|
def generate_random_id ( size = 6 , chars = string . ascii_uppercase + string . digits ) : return "" . join ( random . choice ( chars ) for x in range ( size ) )
| 0 |
generate arbitrary ascii identifier in python
|
Generate random id numbers .
|
cosqa-train-13271
|
def generate_random_id(size=6, chars=string.ascii_uppercase + string.digits):
"""Generate random id numbers."""
return "".join(random.choice(chars) for x in range(size))
|
def _unique_rows_numpy ( a ) : a = np . ascontiguousarray ( a ) unique_a = np . unique ( a . view ( [ ( '' , a . dtype ) ] * a . shape [ 1 ] ) ) return unique_a . view ( a . dtype ) . reshape ( ( unique_a . shape [ 0 ] , a . shape [ 1 ] ) )
| 1 |
python how to keep every 3rd element of array
|
return unique rows
|
cosqa-train-13272
|
def _unique_rows_numpy(a):
"""return unique rows"""
a = np.ascontiguousarray(a)
unique_a = np.unique(a.view([('', a.dtype)] * a.shape[1]))
return unique_a.view(a.dtype).reshape((unique_a.shape[0], a.shape[1]))
|
def mongoqs_to_json ( qs , fields = None ) : l = list ( qs . as_pymongo ( ) ) for element in l : element . pop ( '_cls' ) # use DjangoJSONEncoder for transform date data type to datetime json_qs = json . dumps ( l , indent = 2 , ensure_ascii = False , cls = DjangoJSONEncoder ) return json_qs
| 1 |
generate json to python queryset object
|
transform mongoengine . QuerySet to json
|
cosqa-train-13273
|
def mongoqs_to_json(qs, fields=None):
"""
transform mongoengine.QuerySet to json
"""
l = list(qs.as_pymongo())
for element in l:
element.pop('_cls')
# use DjangoJSONEncoder for transform date data type to datetime
json_qs = json.dumps(l, indent=2, ensure_ascii=False, cls=DjangoJSONEncoder)
return json_qs
|
def _uniform_phi ( M ) : return np . random . uniform ( - np . pi , np . pi , M )
| 1 |
generate random unitary matrix in python
|
Generate M random numbers in [ - pi pi ) .
|
cosqa-train-13274
|
def _uniform_phi(M):
"""
Generate M random numbers in [-pi, pi).
"""
return np.random.uniform(-np.pi, np.pi, M)
|
def getPrimeFactors ( n ) : lo = [ 1 ] n2 = n // 2 k = 2 for k in range ( 2 , n2 + 1 ) : if ( n // k ) * k == n : lo . append ( k ) return lo + [ n , ]
| 1 |
python how to make a list of prime numbers
|
Get all the prime factor of given integer
|
cosqa-train-13275
|
def getPrimeFactors(n):
"""
Get all the prime factor of given integer
@param n integer
@return list [1, ..., n]
"""
lo = [1]
n2 = n // 2
k = 2
for k in range(2, n2 + 1):
if (n // k)*k == n:
lo.append(k)
return lo + [n, ]
|
def generate_unique_host_id ( ) : host = "." . join ( reversed ( socket . gethostname ( ) . split ( "." ) ) ) pid = os . getpid ( ) return "%s.%d" % ( host , pid )
| 1 |
generate short unique id python
|
Generate a unique ID that is somewhat guaranteed to be unique among all instances running at the same time .
|
cosqa-train-13276
|
def generate_unique_host_id():
"""Generate a unique ID, that is somewhat guaranteed to be unique among all
instances running at the same time."""
host = ".".join(reversed(socket.gethostname().split(".")))
pid = os.getpid()
return "%s.%d" % (host, pid)
|
def _dotify ( cls , data ) : return '' . join ( char if char in cls . PRINTABLE_DATA else '.' for char in data )
| 1 |
python how to make dot charcter
|
Add dots .
|
cosqa-train-13277
|
def _dotify(cls, data):
"""Add dots."""
return ''.join(char if char in cls.PRINTABLE_DATA else '.' for char in data)
|
def daterange ( start_date , end_date ) : for n in range ( int ( ( end_date - start_date ) . days ) ) : yield start_date + timedelta ( n )
| 1 |
get a range of dates python
|
Yield one date per day from starting date to ending date .
|
cosqa-train-13278
|
def daterange(start_date, end_date):
"""
Yield one date per day from starting date to ending date.
Args:
start_date (date): starting date.
end_date (date): ending date.
Yields:
date: a date for each day within the range.
"""
for n in range(int((end_date - start_date).days)):
yield start_date + timedelta(n)
|
def _set_scroll_v ( self , * args ) : self . _canvas_categories . yview ( * args ) self . _canvas_scroll . yview ( * args )
| 0 |
python how to make kivy scrollview be at the end
|
Scroll both categories Canvas and scrolling container
|
cosqa-train-13279
|
def _set_scroll_v(self, *args):
"""Scroll both categories Canvas and scrolling container"""
self._canvas_categories.yview(*args)
self._canvas_scroll.yview(*args)
|
def title ( self ) : with switch_window ( self . _browser , self . name ) : return self . _browser . title
| 0 |
get active window title python
|
The title of this window
|
cosqa-train-13280
|
def title(self):
""" The title of this window """
with switch_window(self._browser, self.name):
return self._browser.title
|
def do_restart ( self , line ) : self . application . master . Restart ( opendnp3 . RestartType . COLD , restart_callback )
| 1 |
python how to make something restart
|
Request that the Outstation perform a cold restart . Command syntax is : restart
|
cosqa-train-13281
|
def do_restart(self, line):
"""Request that the Outstation perform a cold restart. Command syntax is: restart"""
self.application.master.Restart(opendnp3.RestartType.COLD, restart_callback)
|
def _get_str_columns ( sf ) : return [ name for name in sf . column_names ( ) if sf [ name ] . dtype == str ]
| 1 |
get all column names in a dataset in python
|
Returns a list of names of columns that are string type .
|
cosqa-train-13282
|
def _get_str_columns(sf):
"""
Returns a list of names of columns that are string type.
"""
return [name for name in sf.column_names() if sf[name].dtype == str]
|
def ma ( self ) : a = self . array return numpy . ma . MaskedArray ( a , mask = numpy . logical_not ( numpy . isfinite ( a ) ) )
| 1 |
python how to mask a numpy array
|
Represent data as a masked array .
|
cosqa-train-13283
|
def ma(self):
"""Represent data as a masked array.
The array is returned with column-first indexing, i.e. for a data file with
columns X Y1 Y2 Y3 ... the array a will be a[0] = X, a[1] = Y1, ... .
inf and nan are filtered via :func:`numpy.isfinite`.
"""
a = self.array
return numpy.ma.MaskedArray(a, mask=numpy.logical_not(numpy.isfinite(a)))
|
def __get_xml_text ( root ) : txt = "" for e in root . childNodes : if ( e . nodeType == e . TEXT_NODE ) : txt += e . data return txt
| 1 |
get all text inside xml python
|
Return the text for the given root node ( xml . dom . minidom ) .
|
cosqa-train-13284
|
def __get_xml_text(root):
""" Return the text for the given root node (xml.dom.minidom). """
txt = ""
for e in root.childNodes:
if (e.nodeType == e.TEXT_NODE):
txt += e.data
return txt
|
def set_context ( self , data ) : for key in data : setattr ( self . local_context , key , data [ key ] )
| 1 |
python how to merge into locals
|
Load Context with data
|
cosqa-train-13285
|
def set_context(self, data):
"""Load Context with data"""
for key in data:
setattr(self.local_context, key, data[key])
|
def get_attribute_name_id ( attr ) : return attr . value . id if isinstance ( attr . value , ast . Name ) else None
| 0 |
get attribute type from python
|
Return the attribute name identifier
|
cosqa-train-13286
|
def get_attribute_name_id(attr):
"""
Return the attribute name identifier
"""
return attr.value.id if isinstance(attr.value, ast.Name) else None
|
def normalize_array ( lst ) : np_arr = np . array ( lst ) x_normalized = np_arr / np_arr . max ( axis = 0 ) return list ( x_normalized )
| 1 |
python how to normalize an array
|
Normalizes list
|
cosqa-train-13287
|
def normalize_array(lst):
"""Normalizes list
:param lst: Array of floats
:return: Normalized (in [0, 1]) input array
"""
np_arr = np.array(lst)
x_normalized = np_arr / np_arr.max(axis=0)
return list(x_normalized)
|
def shape ( self ) : return tuple ( len ( self . _get_axis ( a ) ) for a in self . _AXIS_ORDERS )
| 1 |
get axis size python
|
Return a tuple of axis dimensions
|
cosqa-train-13288
|
def shape(self):
"""
Return a tuple of axis dimensions
"""
return tuple(len(self._get_axis(a)) for a in self._AXIS_ORDERS)
|
def normalize ( self , string ) : return '' . join ( [ self . _normalize . get ( x , x ) for x in nfd ( string ) ] )
| 1 |
python how to normalize text values
|
Normalize the string according to normalization list
|
cosqa-train-13289
|
def normalize(self, string):
"""Normalize the string according to normalization list"""
return ''.join([self._normalize.get(x, x) for x in nfd(string)])
|
def is_bool_matrix ( l ) : if isinstance ( l , np . ndarray ) : if l . ndim == 2 and ( l . dtype == bool ) : return True return False
| 1 |
get boolean matrix similar python
|
r Checks if l is a 2D numpy array of bools
|
cosqa-train-13290
|
def is_bool_matrix(l):
r"""Checks if l is a 2D numpy array of bools
"""
if isinstance(l, np.ndarray):
if l.ndim == 2 and (l.dtype == bool):
return True
return False
|
def fopenat ( base_fd , path ) : return os . fdopen ( openat ( base_fd , path , os . O_RDONLY ) , 'rb' )
| 1 |
python how to open a file in relative path
|
Does openat read - only then does fdopen to get a file object
|
cosqa-train-13291
|
def fopenat(base_fd, path):
"""
Does openat read-only, then does fdopen to get a file object
"""
return os.fdopen(openat(base_fd, path, os.O_RDONLY), 'rb')
|
def array_bytes ( array ) : return np . product ( array . shape ) * np . dtype ( array . dtype ) . itemsize
| 0 |
get bytes size of array python
|
Estimates the memory of the supplied array in bytes
|
cosqa-train-13292
|
def array_bytes(array):
""" Estimates the memory of the supplied array in bytes """
return np.product(array.shape)*np.dtype(array.dtype).itemsize
|
def reopen ( self ) : try : self . _con . reopen ( ) except Exception : if self . _transcation : self . _transaction = False try : self . _con . query ( 'rollback' ) except Exception : pass else : self . _transaction = False self . _closed = False self . _setsession ( ) self . _usage = 0
| 0 |
python how to preserve a connection i
|
Reopen the tough connection .
|
cosqa-train-13293
|
def reopen(self):
"""Reopen the tough connection.
It will not complain if the connection cannot be reopened.
"""
try:
self._con.reopen()
except Exception:
if self._transcation:
self._transaction = False
try:
self._con.query('rollback')
except Exception:
pass
else:
self._transaction = False
self._closed = False
self._setsession()
self._usage = 0
|
def paste ( cmd = paste_cmd , stdout = PIPE ) : return Popen ( cmd , stdout = stdout ) . communicate ( ) [ 0 ] . decode ( 'utf-8' )
| 1 |
get clipboard data with python on linux
|
Returns system clipboard contents .
|
cosqa-train-13294
|
def paste(cmd=paste_cmd, stdout=PIPE):
"""Returns system clipboard contents.
"""
return Popen(cmd, stdout=stdout).communicate()[0].decode('utf-8')
|
def native_conn ( self ) : if self . __native is None : self . __native = self . _get_connection ( ) return self . __native
| 0 |
python how to preserve a connection in
|
Native connection object .
|
cosqa-train-13295
|
def native_conn(self):
"""Native connection object."""
if self.__native is None:
self.__native = self._get_connection()
return self.__native
|
def AmericanDateToEpoch ( self , date_str ) : try : epoch = time . strptime ( date_str , "%m/%d/%Y" ) return int ( calendar . timegm ( epoch ) ) * 1000000 except ValueError : return 0
| 1 |
get date from epoch timestamp python
|
Take a US format date and return epoch .
|
cosqa-train-13296
|
def AmericanDateToEpoch(self, date_str):
"""Take a US format date and return epoch."""
try:
epoch = time.strptime(date_str, "%m/%d/%Y")
return int(calendar.timegm(epoch)) * 1000000
except ValueError:
return 0
|
def runcode ( code ) : for line in code : print ( '# ' + line ) exec ( line , globals ( ) ) print ( '# return ans' ) return ans
| 0 |
python how to print code as its executed
|
Run the given code line by line with printing as list of lines and return variable ans .
|
cosqa-train-13297
|
def runcode(code):
"""Run the given code line by line with printing, as list of lines, and return variable 'ans'."""
for line in code:
print('# '+line)
exec(line,globals())
print('# return ans')
return ans
|
def datetime64_to_datetime ( dt ) : dt64 = np . datetime64 ( dt ) ts = ( dt64 - np . datetime64 ( '1970-01-01T00:00:00' ) ) / np . timedelta64 ( 1 , 's' ) return datetime . datetime . utcfromtimestamp ( ts )
| 1 |
get date from timedelta64 python
|
convert numpy s datetime64 to datetime
|
cosqa-train-13298
|
def datetime64_to_datetime(dt):
""" convert numpy's datetime64 to datetime """
dt64 = np.datetime64(dt)
ts = (dt64 - np.datetime64('1970-01-01T00:00:00')) / np.timedelta64(1, 's')
return datetime.datetime.utcfromtimestamp(ts)
|
def _dump_enum ( self , e , top = '' ) : self . _print ( ) self . _print ( 'enum {} {{' . format ( e . name ) ) self . defines . append ( '{}.{}' . format ( top , e . name ) ) self . tabs += 1 for v in e . value : self . _print ( '{} = {};' . format ( v . name , v . number ) ) self . tabs -= 1 self . _print ( '}' )
| 1 |
python how to print enum key
|
Dump single enum type . Keyword arguments : top -- top namespace
|
cosqa-train-13299
|
def _dump_enum(self, e, top=''):
"""Dump single enum type.
Keyword arguments:
top -- top namespace
"""
self._print()
self._print('enum {} {{'.format(e.name))
self.defines.append('{}.{}'.format(top,e.name))
self.tabs+=1
for v in e.value:
self._print('{} = {};'.format(v.name, v.number))
self.tabs-=1
self._print('}')
|
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